MQL4 and MQL5 Programming Articles

In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.

In part 2, we extend the news filter to protect existing positions during news events. Instead of closing trades, we temporarily remove stop-loss and take-profit levels, storing them safely in memory. When the news window ends, stops are deterministically restored, adjusted if price has already crossed the original levels, while respecting broker minimum distance rules. The result is a mechanism that preserves trade integrity without interfering with entry logic, keeping the EA in control through volatility.

We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.

The Ichimuko Kinko Hyo is a renown Japanese indicator that serves as a trend identification system. We examine this, on a pattern by pattern basis, as has been the case in previous similar articles, and also assess its strategies & test reports with the help of the MQL5 wizard library classes and assembly.

Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.

This guide integrates a trained XGBoost model (ONNX) into an SMC EA to evaluate trade setups before execution. The Python pipeline labels historical XAUUSD events and produces a 12-feature representation aligned with the EA. The result is a reproducible method to train, export, and embed the model so the EA can filter OB, FVG, and BOS signals programmatically.

Traders often face drawdowns from false signals, while waiting for confirmation can lead to missed opportunities. This article introduces a triangular trading strategy using Silver’s pricing in Dollars (XAGUSD) and Euros (XAGEUR), along with the EURUSD exchange rate, to filter out noise. By leveraging cross-market relationships, traders can uncover hidden sentiment and refine their entries in real time.

We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.

Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.

In this article, we will use real-life examples to illustrate posting various types of terminal programs in the MQL5 source code base.

CatBoost is a powerful tree-based machine learning model that specializes in decision-making based on stationary features. Other tree-based models like XGBoost and Random Forest share similar traits in terms of their robustness, ability to handle complex patterns, and interpretability. These models have a wide range of uses, from feature analysis to risk management. In this article, we're going to walk through the procedure of utilizing a trained CatBoost model as a filter for a classic moving average cross trend-following strategy.

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities

This article shows how to build an MQL5 indicator that reads a CSV trading history, extracts Profit($) values and total trades, and computes a cumulative balance progression. We plot the curve in a separate indicator window, auto-scale the Y-axis, and draw horizontal and vertical axes for alignment. The indicator updates on a timer and redraws only when new trades appear. Optional labels display per-trade profit and loss to help assess performance and drawdowns directly on the chart.

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.

The Stochastic Oscillator and the Fractal Adaptive Moving Average are an indicator pairing that could be used for their ability to compliment each other within an MQL5 Expert Advisor. We introduced this pairing in the last article, and now look to wrap up by considering its 5 last signal patterns. In exploring this, as always, we use the MQL5 wizard to build and test out their potential.

When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.

We invite you to get acquainted with the Hierarchical Vector Transformer (HiVT) method, which was developed for fast and accurate forecasting of multimodal time series.

In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.

In this article, we shift focus toward integrating news-driven order execution logic—enabling the EA to act, not just inform. Join us as we explore how to implement automated trade execution in MQL5 and extend the News Headline EA into a fully responsive trading system. Expert Advisors offer significant advantages for algorithmic developers thanks to the wide range of features they support. So far, we’ve focused on building a news and calendar events presentation tool, complete with integrated AI insights lanes and technical indicator insights.

AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.

This article explores the unique identity of each currency pair through the lens of its historical price action. Inspired by the concept of genetic DNA, which encodes the distinct blueprint of every living being, we apply a similar framework to the markets, treating price action as the “DNA” of each pair. By breaking down structural behaviors such as volatility, swings, retracements, spikes, and session characteristics, the tool reveals the underlying profile that distinguishes one pair from another. This approach provides more profound insight into market behavior and equips traders with a structured way to align strategies with the natural tendencies of each instrument.

This article develops a rule-based daily pivot point indicator in MQL5 that uses the previous trading day's high, low, and close values to generate pivot, support, and resistance levels. It details historical data retrieval, pivot computation, chart object management, configurable label rendering, and automatic level updates as new trading days begin. The completed indicator displays multiple historical pivot sessions on the main chart for technical analysis on daily and lower timeframes.

Learn how to build a complete Kagi Chart engine in MQL5—constructing price reversals, generating dynamic line segments, and updating Kagi structures in real time. This first part teaches you how to render Kagi charts directly on MetaTrader 5, giving traders a clear view of trend shifts and market strength while preparing for automated Kagi-based trading logic in Part 2.

The article discusses the Differential Search Algorithm (DSA), which simulates the migration of a superorganism in search of optimal living conditions. The algorithm uses a Gamma distribution to generate a pseudo-stable random walk and offers four strategies for selecting the direction of movement, along with three coordinate mutation mechanisms. How will this method perform?

The article presents an MQL5 method for detecting psychological round numbers by converting prices to strings and counting trailing zeros (ZeroSize). It outlines the theory of institutional liquidity at integers, explains the GetZeroCount logic with tick-size normalization to avoid floating‑point errors, and details hierarchical visualization. Case studies across forex, metals, and crypto, plus timeframe filters and inputs, show how to use confluence and basic risk controls in practice.